Figures for “Sources and consequences of mismatch between leaf disc and whole-leaf leaf mass per area (LMA)”
Masatoshi Katabuchi
May 07, 2022
library(tidyverse)
library(targets)
library(kableExtra)tar_load(sp_mean)
sp_mean |>
group_by(ldlalt_gr) |>
summarise(
lma_leaf = mean(lma_leaf) |> round(1),
lma_disc = mean(lma_disc) |> round(1)
) |>
write_csv("data/ldlalt.csv")1 Leaf disc vs whole-leaf LMA (species-level)
1.1 Pooled
1.2 Seprated
1.3 MCMC check
targets::tar_load(fit_sp_no_lma_summary_model)
targets::tar_load(fit_sp_no_lma_int_summary_model)
targets::tar_load(fit_sp_lma_summary_model)
targets::tar_load(fit_sp_lma2_summary_model2)
targets::tar_load(fit_sp_ld_summary_model)
targets::tar_load(fit_sp_ld2_summary_model2)
targets::tar_load(fit_sp_ld3_summary_model)
targets::tar_load(fit_sp_lm_summary_model)
targets::tar_load(fit_sp_lma0_summary_simple)
targets::tar_load(fit_sp_no_lma_diagnostics_model)
targets::tar_load(fit_sp_no_lma_int_diagnostics_model)
targets::tar_load(fit_sp_lma_diagnostics_model)
targets::tar_load(fit_sp_lma2_diagnostics_model2)
targets::tar_load(fit_sp_ld_diagnostics_model)
targets::tar_load(fit_sp_ld2_diagnostics_model2)
targets::tar_load(fit_sp_ld3_diagnostics_model)
targets::tar_load(fit_sp_lm_diagnostics_model)
targets::tar_load(fit_sp_lma0_diagnostics_simple)fit_sp_no_lma_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_no_lma_int_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_lma_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_lma2_summary_model2 |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_ld_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_ld2_summary_model2 |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_ld3_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_lm_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_lma0_summary_simple |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>It is difficult to model heteroskedasticity and measurement errors at the same time.
div_check(fit_sp_no_lma_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_no_lma_int_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_lma_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_lma2_diagnostics_model2)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_ld_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_ld2_diagnostics_model2)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_ld3_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_lm_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_lma0_diagnostics_simple)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"targets::tar_load(loo_model)
loo::loo_compare(
loo_model[[1]], loo_model[[2]], loo_model[[3]],
loo_model[[4]], loo_model[[5]], loo_model[[6]],
loo_model[[7]], loo_model[[8]], loo_model[[9]],
loo_model[[10]], loo_model[[11]],
loo_model[[12]], loo_model[[13]]
)
#> elpd_diff se_diff
#> model10 0.0 0.0
#> model11 -3.0 4.2
#> model12 -4.7 3.4
#> model7 -12.7 6.3
#> model13 -13.2 5.6
#> model9 -14.1 6.8
#> model6 -14.6 6.5
#> model3 -14.9 6.9
#> model4 -15.8 6.6
#> model5 -21.3 8.4
#> model8 -21.3 8.4
#> model1 -24.1 7.3
#> model2 -25.5 7.2tar_read(cv_sp)
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.4852 0.9108
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.007418 0.036529 0.028880
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.09834 0.06599 0.03733 0.04426
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 0.64625 0.88001 0.03052 0.100161.4 coef
2 SMA table
2.1 species
tar_load(sma_sp_tab)
sma_sp_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.97 [0.94, 1.01] | 0.09 [0.02, 0.16] | 0.87 |
| Thick~Large | 0.93 [0.85, 1.03] | 0.19 [0.01, 0.37] | 0.86 |
| Thick~Small | 1.02 [0.97, 1.07] | 0 [-0.11, 0.11] | 0.93 |
| Thin~Large | 0.91 [0.82, 1.02] | 0.2 [0.02, 0.37] | 0.71 |
| Thin~Small | 0.93 [0.83, 1.03] | 0.16 [-0.02, 0.35] | 0.84 |
sma_sp_tab |>
write_csv("data/sma_sp_tab.csv")2.2 species LD
tar_load(sma_sp_ld_tab)
sma_sp_ld_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.97 [0.94, 1.01] | 0.09 [0.02, 0.16] | 0.87 |
| DenseThickLarge | 0.96 [0.82, 1.13] | 0.14 [-0.15, 0.43] | 0.79 |
| DenseThickSmall | 1.02 [0.94, 1.11] | -0.01 [-0.17, 0.15] | 0.90 |
| DenseThinLarge | 0.8 [0.66, 0.98] | 0.37 [0.09, 0.64] | 0.60 |
| DenseThinSmall | 0.86 [0.72, 1.03] | 0.27 [-0.01, 0.55] | 0.81 |
| NondenseThickLarge | 0.75 [0.59, 0.94] | 0.58 [0.22, 0.94] | 0.72 |
| NondenseThickSmall | 0.99 [0.87, 1.13] | 0.05 [-0.23, 0.32] | 0.82 |
| NondenseThinLarge | 0.77 [0.65, 0.91] | 0.49 [0.25, 0.74] | 0.56 |
| NondenseThinSmall | 0.87 [0.71, 1.06] | 0.28 [-0.06, 0.62] | 0.68 |
sma_sp_ld_tab |>
write_csv("data/sma_sp_ld_tab.csv")2.3 species simple
tar_load(sma_sp_2_tab)
sma_sp_2_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.97 [0.94, 1.01] | 0.09 [0.02, 0.16] | 0.87 |
| Dense | 0.97 [0.91, 1.04] | 0.08 [-0.03, 0.2] | 0.84 |
| Less dense | 0.93 [0.87, 0.99] | 0.19 [0.08, 0.3] | 0.85 |
| Large | 0.96 [0.9, 1.03] | 0.12 [0, 0.24] | 0.80 |
| Small | 1 [0.96, 1.05] | 0.03 [-0.06, 0.11] | 0.92 |
| Thick | 0.98 [0.94, 1.03] | 0.08 [-0.02, 0.17] | 0.90 |
| Thin | 0.92 [0.85, 0.99] | 0.19 [0.06, 0.32] | 0.75 |
sma_sp_2_tab |>
write_csv("data/sma_sp_2_tab.csv")2.4 tree
tar_load(sma_tree_tab)
sma_tree_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.96 [0.94, 0.99] | 0.11 [0.06, 0.16] | 0.72 |
| Thick~Large | 0.97 [0.9, 1.04] | 0.12 [-0.03, 0.26] | 0.61 |
| Thick~Small | 0.94 [0.9, 0.98] | 0.16 [0.07, 0.24] | 0.79 |
| Thin~Large | 0.91 [0.84, 0.97] | 0.21 [0.09, 0.32] | 0.45 |
| Thin~Small | 0.91 [0.85, 0.98] | 0.19 [0.07, 0.31] | 0.66 |
2.5 tree LD
tar_load(sma_tree_ld_tab)
sma_tree_ld_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.96 [0.94, 0.99] | 0.11 [0.06, 0.16] | 0.72 |
| DenseThickLarge | 0.89 [0.79, 1] | 0.25 [0.06, 0.44] | 0.49 |
| DenseThickSmall | 0.91 [0.85, 0.97] | 0.21 [0.1, 0.32] | 0.76 |
| DenseThinLarge | 0.75 [0.67, 0.85] | 0.44 [0.29, 0.6] | 0.34 |
| DenseThinSmall | 0.8 [0.72, 0.9] | 0.36 [0.2, 0.52] | 0.65 |
| NondenseThickLarge | 0.74 [0.64, 0.86] | 0.61 [0.38, 0.83] | 0.43 |
| NondenseThickSmall | 0.84 [0.78, 0.91] | 0.38 [0.24, 0.53] | 0.68 |
| NondenseThinLarge | 0.73 [0.65, 0.81] | 0.57 [0.42, 0.72] | 0.28 |
| NondenseThinSmall | 0.89 [0.78, 1.01] | 0.25 [0.03, 0.46] | 0.38 |
3 Validation
tar_load(cv_sp)
cv_sp
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.4852 0.9108
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.007418 0.036529 0.028880
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.09834 0.06599 0.03733 0.04426
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 0.64625 0.88001 0.03052 0.10016tar_load(cv_tree)
cv_tree
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.718 0.0421
#> 2 fit 2 0.684 0.0467
#> 3 fit 3 0.702 0.0440
#> 4 fit 4 0.739 0.0385
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.8911 0.8176
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.05804 0.03071 0.05749
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.30044 0.16541 0.03573 0.11701
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 1.54932 0.72303 0.01379 0.218864 Leaf disc vs whole-leaf LMA (individual-level)
5 Divergence (species-level)
6 LMA and LD (species-level)
7 CV
8 Leaf support cost (species-level)
9 Computing Environment
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.3 (2022-03-10)
#> os Ubuntu 20.04.4 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Etc/UTC
#> date 2022-05-07
#> pandoc 2.16.2 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> ! package * version date (UTC) lib source
#> P abind 1.4-5 2016-07-21 [?] RSPM (R 4.1.0)
#> P assertthat 0.2.1 2019-03-21 [?] RSPM (R 4.1.3)
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